An MDL-based wavelet scattering features selection for signal classification

Wavelet scattering is a redundant time-frequency transform that was shown to be a powerful tool in signal classification. It shares the convolutional architecture with convolutional neural networks, but it offers some advantages, including faster training and small training sets. However, it introduces some redundancy along the frequency axis, especially for filters that have a high degree of overlap. This naturally leads to a need for dimensionality reduction to further increase its efficiency as a machine learning tool. In this paper, the Minimum Description Length is used to define an automatic procedure for optimizing the selection of the scattering features, even in the frequency domain. The proposed study is limited to the class of uniform sampling models. Experimental results show that the proposed method is able to automatically select the optimal sampling step that guarantees the highest classification accuracy for fixed transform parameters, when applied to audio/sound signals

An entropy based approach for SSIM speed up

This paper focuses on an entropy based formalism to speed up the evaluation of the Structural SIMilarity(SSIM) index in images affected by a global distortion. Looking at images as information sources, avisualdistortion typical setcan be defined for SSIM. This typical set consists of just a subset of information belongingto the original image and the corresponding one in the distorted version. As side effect, some general theoreticalcriteria for the computation of any full reference quality assessment measure can be given in order to maximizeits computational efficiency. Experimental results on various test images show that the proposed approachallows to estimate SSIM with a considerable speed up (about 200 times) and a small relative error (often lowerthan 5%